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Multistage Interconnection Networks (MINs) are a class of network systems designed to improve communication in large‐scale parallel processing systems. These networks facilitate the communications to perform a single overall task in a parallel processing system consisting of a large number of processors that are working together. The purpose of this paper is to discuss two types of MINs: gamma networks and extra‐stage gamma networks. It is shown that a specific modification in the structure of a standard gamma network will add multiple paths from a specific source to a specific destination.

The terminal reliability of these networks are evaluated and analyzed in terms of the number of their paths connecting a source i, i=1, 2, … , N to any terminal. Numerical examples are also given to demonstrate each network's performance.

In this paper, terminal reliability as a function of the reliability of a switching element of MINs is analyzed. Terminal reliability, generally used as a measure of robustness of a MIN, is the probability of existence of at least one fault free path between a designated pair of input (s) and output (t) terminals. The fault‐tolerance and terminal reliability capabilities as well as the reliability of these networks are evaluated. It is observed that the additional stage provides more redundant paths in the networks. Therefore, an additional stage leads to extra paths and improves the system's fault tolerance. It has been shown that in a Shuffle‐Exchange Network Systems, an addition of an extra stage leads to higher terminal reliability of that network. However, the additional stage does not necessarily improve the terminal reliability of the gamma network. The additional stage could add to the switch complexity and increase the probability of a path failure as well. Therefore, the extra‐stage gamma network has multiple paths in every source‐destination pair including the case when the tag value is 0, that is, when (s=t).

Other authors have not done this kind of research analysis.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

A new method of representing non‐spherical, smooth‐surfaced, axi‐symmetrical particles in discrete element (DE) simulation using model particles comprising overlapping spheres of arbitrary size whose centres are fixed in position relative to each other along the major axis of symmetry of the particle is presented. Contact detection and calculation of force‐deformation and particle movement is achieved using standard DE techniques modified to integrate the behaviour of each element sphere with that of the multi‐element particle to which it belongs. The method enables the dynamic behaviour of particles of high aspect ratio and irregular curvature (in two dimensions) to be modelled. The use of spheres to represent a particle takes advantage of the computational speed and accuracy of contact detection for spheres, which should make the method comparable in computational efficiency to alternative schemes for representing non‐spherical particles.

This paper aims to present a new hybrid algorithm of Particle Swarm Optimization and the Genetic Algorithm (PSOGA) to optimize the space trusses with continuous design variables. The PSOGA is an efficient hybridized algorithm to solve optimization problems.

These algorithms have shown outstanding performance in solving optimization problems with continuous variables. The PSO conceptually models the social behavior of birds, in which individual birds exchange information about their position, velocity and fitness. The behavior of a flock is influencing the probability of migration to other regions with high fitness. The GAs procedure is based on the mechanism of natural selection. The present study uses mutation, random selection and reproduction to reach the best genetic algorithm by the operators of natural genetics. Thus, only identical chromosomes or particles can be converged.

In this research, using the idea of hybridization PSO and GA algorithms are hybridized and a new meta-heuristic algorithm is developed to minimize the space trusses with continuous design variables. To showing the efficiency and robustness of the new algorithm, several benchmark problems are solved and compared with other researchers.

The results indicate that the hybrid PSO algorithm improved in both exploration and exploitation. The PSO algorithm can be used to minimize the weight of structural problems under stress and displacement constraints.

This study scrutinizes the attitudes of postgraduate students specializing in education toward research at one University of Education in Myanmar.

Having collected data from 121 Master in Education (MEd) students in three education departments, the study validated Papanastasiou's (2014) Revised-Attitude toward Research (R-ATR) scale by performing confirmatory factor analysis (CFA). A one-way MANOVA was used to establish the significance of mean differences between groups and ANOVA was used for the same purpose among groups.

The R-ATR scale is fit with the sample (χ²/df = 1.60), and the students' attitudes toward research are favorable with mean value for research usefulness (5.98), positive research predispositions (5.55) and research anxiety (3.61), despite feeling stressed and anxious. There was a significant difference in the students' attitudes toward research regarding their departments, while no significant difference was found based on their research experience, gender and age.

There should be an effective mechanism in implementing the research course and in online delivery mode, collaboration between the faculties in the process of revision and review of the research courses, effective strategies of improving research instructors' quality and also internationalization to sustain students' positive attitudes toward research and to reduce their stress and anxiety.

This research is empirically novel, and the initiative attempts to first use the R-ATR scale in teacher education in Myanmar.